Controlexperient
Read and preprocess control-data
if (!require("pacman")) install.packages("pacman")
library(pacman)
p_load(tidyverse)
p_load(R.utils)
sourceDirectory("./source/")
data<-readControl() %>% preproControl() check accuracy
corrResp: correct response rtResp: reaction time of response - inSet: 0 - correct rejection (true negativ) - inSet: 1 - Hit (true positiv) - session: 1-5 - time factor (not constant, therefore only ordinal) - kategory: Landscape, Face, Art
p_load(dplyr)
p_load(brms)
options (mc.cores=parallel::detectCores ()) # Run on multiple cores
p_load(sjPlot)
group_by(data,kategory,session) %>%
summarize(mean_corr= mean(corrResp),sd_corr=sd(corrResp),mean_RT=mean(rtResp),sd_RT=sd(rtResp))p <- ggplot(data=data,
aes(x = corrResp, y =rtResp, color= inSet) )+geom_point()
p+facet_grid(kategory ~ session,margins = T)
p+facet_grid(kategory ~ participant)
#correct response
fit_corrRespSession<-lm(corrResp~session*kategory,data = data)
summary(fit_corrRespSession)##
## Call:
## lm(formula = corrResp ~ session * kategory, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90 -0.50 0.20 0.35 0.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.000e-01 1.090e-01 4.588 8.83e-06 ***
## session2 2.000e-01 1.888e-01 1.059 0.291
## session3 9.043e-16 1.888e-01 0.000 1.000
## session4 2.633e-16 1.888e-01 0.000 1.000
## session5 -1.000e-01 1.888e-01 -0.530 0.597
## kategoryFace 1.500e-01 1.541e-01 0.973 0.332
## kategoryLandscape 1.500e-01 1.541e-01 0.973 0.332
## session2:kategoryFace -1.500e-01 2.670e-01 -0.562 0.575
## session3:kategoryFace 1.500e-01 2.670e-01 0.562 0.575
## session4:kategoryFace 5.000e-02 2.670e-01 0.187 0.852
## session5:kategoryFace 3.500e-01 2.670e-01 1.311 0.192
## session2:kategoryLandscape -2.500e-01 2.670e-01 -0.936 0.350
## session3:kategoryLandscape 1.500e-01 2.670e-01 0.562 0.575
## session4:kategoryLandscape -5.000e-02 2.670e-01 -0.187 0.852
## session5:kategoryLandscape -5.000e-02 2.670e-01 -0.187 0.852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4874 on 165 degrees of freedom
## Multiple R-squared: 0.06802, Adjusted R-squared: -0.01105
## F-statistic: 0.8602 on 14 and 165 DF, p-value: 0.603sjp.lm(fit = fit_corrRespSession)
fit_corrRespSet<-lm(corrResp~kategory*inSet,data = data)
summary(fit_corrRespSet)##
## Call:
## lm(formula = corrResp ~ kategory * inSet, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.93333 -0.10000 0.06667 0.20000 0.90000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.93333 0.07267 12.843 < 2e-16 ***
## kategoryFace -0.13333 0.10277 -1.297 0.196
## kategoryLandscape -0.50000 0.10277 -4.865 2.55e-06 ***
## inSet1 -0.83333 0.10277 -8.109 8.80e-14 ***
## kategoryFace:inSet1 0.70000 0.14534 4.816 3.17e-06 ***
## kategoryLandscape:inSet1 1.23333 0.14534 8.486 9.06e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.398 on 174 degrees of freedom
## Multiple R-squared: 0.3446, Adjusted R-squared: 0.3258
## F-statistic: 18.3 on 5 and 174 DF, p-value: 1.415e-14sjp.int(fit = fit_corrRespSet, type = "eff")
fit_corrRespParticipant<-lm(corrResp~kategory*participant,data = data)
summary(fit_corrRespParticipant)##
## Call:
## lm(formula = corrResp ~ kategory * participant, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.78 -0.50 0.22 0.34 0.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6000 0.1510 3.972 0.000104 ***
## kategoryFace -0.1000 0.2136 -0.468 0.640279
## kategoryLandscape -0.1000 0.2136 -0.468 0.640279
## participantug93 -0.1000 0.1655 -0.604 0.546398
## kategoryFace:participantug93 0.3800 0.2340 1.624 0.106209
## kategoryLandscape:participantug93 0.2600 0.2340 1.111 0.268065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4777 on 174 degrees of freedom
## Multiple R-squared: 0.05614, Adjusted R-squared: 0.02901
## F-statistic: 2.07 on 5 and 174 DF, p-value: 0.07141sjp.int(fit = fit_corrRespParticipant, type = "eff")
# reaction time
fit_rtResp<-lm(rtResp~kategory*inSet, data)
summary(fit_rtResp)##
## Call:
## lm(formula = rtResp ~ kategory * inSet, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8132 -0.4043 -0.1457 0.3317 2.6404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.16506 0.10856 10.732 <2e-16 ***
## kategoryFace -0.20441 0.15353 -1.331 0.185
## kategoryLandscape 0.03700 0.15353 0.241 0.810
## inSet1 -0.06813 0.15353 -0.444 0.658
## kategoryFace:inSet1 0.01956 0.21712 0.090 0.928
## kategoryLandscape:inSet1 -0.34753 0.21712 -1.601 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5946 on 174 degrees of freedom
## Multiple R-squared: 0.05955, Adjusted R-squared: 0.03252
## F-statistic: 2.203 on 5 and 174 DF, p-value: 0.05605sjp.int(fit = fit_rtResp,type = "eff")
fit_rtRespSession<-lm(rtResp~kategory*session, data)
summary(fit_rtRespSession)##
## Call:
## lm(formula = rtResp ~ kategory * session, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9656 -0.3469 -0.1126 0.2249 2.5806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.18677 0.12971 9.149 <2e-16 ***
## kategoryFace -0.21495 0.18344 -1.172 0.2430
## kategoryLandscape -0.44019 0.18344 -2.400 0.0175 *
## session2 0.38131 0.22467 1.697 0.0915 .
## session3 -0.29029 0.22467 -1.292 0.1981
## session4 -0.37214 0.22467 -1.656 0.0995 .
## session5 -0.05353 0.22467 -0.238 0.8120
## kategoryFace:session2 -0.32967 0.31773 -1.038 0.3010
## kategoryLandscape:session2 -0.15484 0.31773 -0.487 0.6267
## kategoryFace:session3 0.04646 0.31773 0.146 0.8839
## kategoryLandscape:session3 0.59178 0.31773 1.863 0.0643 .
## kategoryFace:session4 0.16379 0.31773 0.515 0.6069
## kategoryLandscape:session4 0.57675 0.31773 1.815 0.0713 .
## kategoryFace:session5 0.24132 0.31773 0.760 0.4486
## kategoryLandscape:session5 0.80687 0.31773 2.539 0.0120 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5801 on 165 degrees of freedom
## Multiple R-squared: 0.1512, Adjusted R-squared: 0.07918
## F-statistic: 2.099 on 14 and 165 DF, p-value: 0.01418sjp.int(fit = fit_rtRespSession,type = "eff")## `sjp.int()` will become deprecated in the future. Please use `plot_model()` instead.
fit_rtRespParticipant<-lm(rtResp~kategory*participant, data)
summary(fit_rtRespParticipant)##
## Call:
## lm(formula = rtResp ~ kategory * participant, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7837 -0.4254 -0.1708 0.3078 2.5689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2803 0.1907 6.715 2.57e-10 ***
## kategoryFace -0.2968 0.2697 -1.101 0.2726
## kategoryLandscape -0.5202 0.2697 -1.929 0.0553 .
## participantug93 -0.1792 0.2089 -0.858 0.3921
## kategoryFace:participantug93 0.1226 0.2954 0.415 0.6787
## kategoryLandscape:participantug93 0.4601 0.2954 1.558 0.1211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.603 on 174 degrees of freedom
## Multiple R-squared: 0.03288, Adjusted R-squared: 0.005087
## F-statistic: 1.183 on 5 and 174 DF, p-value: 0.3194sjp.int(fit = fit_rtRespParticipant,type = "eff")
fitBrmCorrResp<-brm(corrResp~inSet*kategory+(1|session)+(1|participant)+(1|id),data = data,family = bernoulli())## Compiling the C++ model## Start samplingsummary(fitBrmCorrResp)## Family: bernoulli
## Links: mu = logit
## Formula: corrResp ~ inSet * kategory + (1 | session) + (1 | participant) + (1 | id)
## Data: data (Number of observations: 180)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## ICs: LOO = NA; WAIC = NA; R2 = NA
##
## Group-Level Effects:
## ~id (Number of levels: 90)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.84 0.64 0.04 2.45 247 1.02
##
## ~participant (Number of levels: 2)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 2.51 2.34 0.07 8.39 87 1.05
##
## ~session (Number of levels: 5)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.40 0.42 0.01 1.49 1449 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## Intercept 2.81 2.09 -1.79 6.95 39
## inSet1 -5.86 1.44 -9.25 -3.59 240
## kategoryFace -1.62 1.03 -3.81 0.32 800
## kategoryLandscape -3.63 1.11 -6.27 -1.87 331
## inSet1:kategoryFace 4.97 1.53 2.24 8.33 559
## inSet1:kategoryLandscape 8.13 1.81 5.32 12.51 240
## Rhat
## Intercept 1.11
## inSet1 1.02
## kategoryFace 1.01
## kategoryLandscape 1.01
## inSet1:kategoryFace 1.01
## inSet1:kategoryLandscape 1.02
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fitBrmCorrResp)
plot(fitBrmCorrResp)
fitBrmRt<-brm(rtResp~inSet*kategory+(1|session)+(1|participant)+(1|id),data = data,family =exgaussian(link = "identity", link_sigma = "log", link_beta = "log"))## Compiling the C++ model
## Start samplingsummary(fitBrmRt)## Family: exgaussian
## Links: mu = identity; sigma = identity; beta = identity
## Formula: rtResp ~ inSet * kategory + (1 | session) + (1 | participant) + (1 | id)
## Data: data (Number of observations: 180)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
## ICs: LOO = NA; WAIC = NA; R2 = NA
##
## Group-Level Effects:
## ~id (Number of levels: 90)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.08 0.05 0.01 0.19 11 1.13
##
## ~participant (Number of levels: 2)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.91 1.19 0.01 3.66 4 1.73
##
## ~session (Number of levels: 5)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.08 0.07 0.00 0.25 696 1.01
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## Intercept 0.49 0.25 -0.05 1.11 254
## inSet1 -0.02 0.10 -0.23 0.14 12
## kategoryFace -0.14 0.08 -0.31 0.01 615
## kategoryLandscape 0.02 0.08 -0.16 0.17 645
## inSet1:kategoryFace -0.00 0.15 -0.22 0.29 9
## inSet1:kategoryLandscape -0.10 0.13 -0.32 0.16 11
## Rhat
## Intercept 1.03
## inSet1 1.14
## kategoryFace 1.02
## kategoryLandscape 1.01
## inSet1:kategoryFace 1.22
## inSet1:kategoryLandscape 1.13
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.09 0.04 0.01 0.16 283 1.01
## beta 0.59 0.05 0.50 0.70 4000 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).marginal_effects(fitBrmRt)
plot(fitBrmRt)
